Micro-moment analysis

ABSTRACT

A computer-implemented method for determining a micro-moment value that indicates an optimal time for a customer to receive a targeted advertisement. The method includes receiving, via a network, customer data associated with behavior of a plurality of customers. The method includes determining, via one or more processors, a micro-moment value predicting an optimal time and network location to engage a customer based on the customer data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.15/269,786, filed Sep. 19, 2016 entitled “Micro-Moment Analysis” andU.S. application Ser. No. 15/269,642, filed Sep. 19, 2016 entitled“Universal Identification” which applications claim priority from U.S.Provisional Application No. 62/220,727, filed Sep. 18, 2015, and to U.S.Provisional Application No. 62/288,763, filed Jan. 29, 2016. Thedisclosures of the applications referenced above are incorporated byreference herein in their entirety.

FIELD OF TECHNOLOGY

The present disclosure relates to the analysis of data using machinelearning and other artificial intelligence algorithms and deliveringthat data to end users who are identified by the algorithms.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

We are in the age of customer empowerment, where customers expect brandsto be connected and relevant and to meet their needs at everyinteraction. To do so, leading marketers are taking a customer-obsessedapproach to transforming their business by investing in the data,talents, tools and strategies needed to respond to the needs of theconnected customer.

Today, many brands struggle to keep pace with the volume, velocity andvariety of data in order to meet customer expectations. According toGartner research, 90% of the world's data has been created during thepast two years. This growth and availability of data has led to anexpectation of data usage to enable relevant, personalized experiences.It is not enough to collect and structure the data, it must be actedupon. Consumers expect timely, relevant and seamless brand experiences.Therefore, brands must anticipate and predict their customers' needs,habit, trends and preferences to engage their customers in 1:1conversation at the right moment of decision making.

As the connected economy marches forward at an accelerating pace, datais proving to be marketing's most valuable currency. Oceans of datagenerated from the Internet of Things (IoT) will magnify both theproblem and the opportunity. But additional data lacks value if it can'tbe reduced to useful insight that informs a unique, differentiated brandexperience.

A phenomenon described as “data paralysis” keeps much of the dataunused. While the opportunities are vast, as the volume, variety andvelocity of data generated from a connected economy explodes, digitalmarketing becomes increasingly difficult. By some measures, the amountof stored information grows four times faster than the world economy,while the processing power of computers grows nine times faster. It'slittle wonder marketers struggle with information overload thatironically reduces them to data paralysis where the benefits from dataare never fully realized. In part, this scenario stems from a lack ofthe right mix of algorithms and technology for translating big data toactionable intelligence.

SUMMARY

Features and advantages described in this summary and the followingdetailed description are not all-inclusive. Many additional features andadvantages will be apparent to one of ordinary skill in the art in viewof the drawings, specification, and claims hereof. Additionally, otherembodiments may omit one or more (or all) of the features and advantagesdescribed in this summary.

A Machine Intelligence Platform provides a way forward from dataparalysis and takes full advantage of technology breakthroughs inmachine learning algorithms, deep learning networks and ArtificialIntelligence (AI). The Machine Intelligence Platform may shift fromdata-driven marketing to intelligent marketing, where everyday decisionsare informed by potentially billions of data points, rather than guessesand assumptions. Working with huge sources of structured,semi-structured and unstructured data, the platform may ingest, analyzeand compare the data. Machine-learning algorithms can providestatistical evidence that something might be wrong or right based on howmany past occurrences of similar patterns exist.

A “micro-moments” strategy to data analysis may provide further insight.As customers consume content, interact with each other and engage Inmultiple, simultaneous conversations, marketers have the challenge ofdetermining the optimal moment to engage. During these optimalopportunities, known as micro-moments, the buyer may decide to eithercontinue or abandon a relationship with the brand. Each micro-moment maybe considered a “fingerprint” of a user's online activity that may betracked across multiple websites and devices. Such micro-moments arereal-time, intent-driven events that are a critical opportunity forbrands to shape customer decisions and preferences. Each micro-momentmay be analyzed as data container or “radian” for purposes of analyzinga customer's complete online journey, as described herein.

In some embodiments, a computer system or a computer-implemented methodmay determine a micro-moment value. The system or method may receive aplurality of signals from a plurality of online actions. The pluralityof signals may correspond to an online profile for a consumer (p), andeach of the plurality of signals may include one or more of a pastduration, a past network location, a past time period, and a past actiontype. The system and method may also determine a predicted micro-momentvalue based on the plurality of signals, the micro-moment valuepredicting one or more of a future duration, a future network location,and a future time period for a further online action for the consumer.The micro-moment value may consist of:

${MMV}_{1} = {\sum\limits_{c = 1}^{p}{\sum\limits_{e = 1}^{y}{\left( \frac{RCV}{Y_{i,t}} \right)*\left( \frac{\Pi \; {radians}}{channel} \right)}}}$

wherein:

$\sum\limits_{c = 1}^{p}$

includes a conversion index (c) for the plurality of signals thatinclude a conversion (c=1) across the plurality of online profiles (p),and consists of:

$\sum\limits_{{{Conversion}\mspace{14mu} {{index}{(c)}}} = 1}^{{Total}\mspace{14mu} {Number}\mspace{14mu} {of}\mspace{14mu} {{Profiles}{(p)}}}.$

The value for

$\sum\limits_{e = 1}^{y}\left( \frac{RCV}{Y_{i,t}} \right)$

may include a click rate through domains, wherein the click rate throughdomains consists of:

$\sum\limits_{{{Customer}\mspace{14mu} {Experience}\mspace{14mu} {{Value}{(e)}}} = {1\mspace{14mu} {or}\mspace{14mu} 0}}^{{Click}\mspace{14mu} {{Index}{(y)}}}\frac{{Real}\text{-}{time}\mspace{14mu} {Customer}\mspace{14mu} {Journey}\mspace{14mu} {{Value}({RCV})}}{{Contextual}\mspace{14mu} {Signals}\mspace{14mu} {{Value}(Y)}}$

and the click index (y) includes a primary key for each of the pluralityof signals, the customer experience value includes a value of 1 for eachof the plurality of signals that includes the conversion or a value of 0for each of the plurality of signals that does not include theconversion, the RCV includes an index value for each of the plurality ofsignals, and Y includes a value based on the past action type.Likewise, the value for:

$\left( \frac{\Pi \mspace{14mu} {radians}}{channel} \right)$

includes a click rate per channel and the click rate per channelconsists of:

$\left( \frac{\Pi \mspace{14mu} {radian}\mspace{14mu} {value}}{channel} \right)$

the radian value consists of a value based on a total number for theplurality of signals and the channel equals a value corresponding to atype of device accessing the domain. Further, the system or method mayengage the customer when the micro-moment value reaches a duration and atime period corresponding to a predicted key moment in a plot of thepredicted micro-moment values, wherein the micro moment value indicatesan optimal time and network location to engage the customer.

The Machine Intelligence Platform may map the entire customer journeyacross devices and channels to predict how the consumer wants tointeract with the brand and personalize each moment for the consumer. Asthe volume of data grows, intelligence extracted using a Micro-MomentsValue Algorithm can ingest and activate data from multiple sources toprovide insights that allow us to tap into micro-moments in theconsumer's journey.

Real-time cognitive commerce may enable marketers to customize theshopping experience by supporting individually tuned merchandising,product recommendations, personalized search and guided navigation.Machine learning capabilities may support delivery of targeted anddynamic pricing and promotion. Algorithms may learn shopping behavior inreal time to update the relevance of the customer experience as itoccurs.

Machine learning may unlock the power of data and deliver highlycustomized experiences. In some embodiments, the Machine IntelligencePlatform may determine the best available assets, the right creative,message, offer, and call-to-action at the right moment using real-timecustomer insights and customer-level attribution. Data-driven audiencesegments may be dynamically created and activated across the marketer'sCommerce, Media and Customer Engagement channels.

Within the Machine Intelligence Platform, a High-Frequency IntelligenceHub may process millions of signals and personalized streams of data tocustomize and activate targeted communication across channels, lettingmarketers engage with customers when they are shown to be most receptiveto a message. This enables marketers and brands to useIntelligence-as-a-Service to inform Media, Commerce, CRM and CustomerExperience simultaneously while providing insight and intelligence.

Further, a universal identification graph algorithm may help marketersconnect identities across devices and channels (i.e., across a devicesuch as a mobile phone, desktop, TV, etc.) to one customer. Theuniversal identification may allow marketers to seamlessly and securelyengage customers with relevant brand experience as they move betweendevices and across all digital touch-points.

In one embodiment, the disclosure describes a computer-implementedmethod for determining a micro-moment value. The method may includereceiving, via a network, customer data associated with behavior of aplurality of customers. The method may also include determining, via oneor more processors, a signal of a click rate through domains based onthe customer data. The method may also include determining, via the oneor more processors, a radian rate per channel based on the customerdata, and determining, via the one or more processors, a conversion ratebased on the signal of the click rate through domains. The method mayalso include determining, via the one or more processors, a micro-momentvalue based on the radian rate per channel and the conversion rate.

In further embodiments, the disclosure described a digital marketingplatform comprising at least one processor and at least one memorystoring computer executable instructions that, when executed by the atleast one processor, cause the apparatus at least to perform a method.The method may include receiving, via a network, customer dataassociated with behavior of a plurality of customers. The method mayalso include determining, via one or more processors, a signal of aclick rate through domains based on the customer data. The method mayalso include determining, via the one or more processors, a radian rateper channel based on the customer data, and determining, via the one ormore processors, a conversion rate based on the signal of the click ratethrough domains. The method may also include determining, via the one ormore processors, a micro-moment value based on the radian rate perchannel and the conversion rate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for linking dynamic information to a digitalmarketing platform as described herein;

FIG. 2 illustrates an embodiment of an artificial neural network modelas described herein;

FIG. 3 illustrates an example customer journey use case as describedherein;

FIG. 4 illustrates a graph of example micro-moment values plotted overtime;

FIG. 5 illustrates an exemplary process flow for determining amicro-moment value for use with the system for linking dynamicinformation to a digital marketing platform as described herein;

FIG. 6 illustrates an exemplary computing device used within the systemfor linking dynamic information to a digital marketing platform and toimplement the various process flows or methods described herein; and

FIG. 7 illustrates an exemplary structure for the system for determininga micro-market value across multiple computer network devices andchannels as described herein.

The figures depict a preferred embodiment for purposes of illustrationonly. One skilled in the art may readily recognize from the followingdiscussion that alternative embodiments of the structures and methodsillustrated herein may be employed without departing from the principlesdescribed herein.

DETAILED DESCRIPTION

FIG. 1 generally illustrates one embodiment of a system 100 for creatingand using a digital marketing platform as described herein. The system100 may include front end components 102 (e.g., a merchant digitalcontent system 104, customer digital content browser 106, etc.) andbackend components 110 (e.g., a digital marketing platform 112). Thefront end components 102 and backend components 110 may be incommunication with each other via a communication link 111 (e.g.,computer network, internet connection, etc.). The system 100 may includevarious software or computer-executable instructions and specializedhardware components or modules that employ the software and instructionsto provide a digital marketing platform with dynamic data records asdescribed herein. The various modules may be implemented ascomputer-readable storage memories containing computer-readableinstructions (i.e., software) for execution by a processor of thecomputer system 100 within a specialized or unique computing device. Themodules may perform the various tasks associated with creating and usinga digital marketing platform as described herein. The system 100 mayalso include both hardware and software applications, as well as variousdata communications channels for communicating data between the variousspecialized or unique front end 102 and back end 110 hardware andsoftware components.

Generally, in some embodiments, the digital marketing platform 112 andother backend components 110 may receive and store various types ofconsumer data gathered from one or more customer digital contentbrowsers 106 via a network 111 or otherwise. In some embodiments, dataon groups of consumers or on particular consumers may be received viathe merchant digital content system 104 of one or more merchants (onlyone merchant digital content system shown in FIG. 1 for purposesillustration, but more merchants are contemplated in some embodiments).The digital marketing platform 112 may, in some embodiments, analyze thedata in ways that are useful to predicting consumer behavior, such asmaking online purchases, so as to engage with the customer at a timethat may influence those purchase or other decisions. In someembodiments, and as described herein, the gathered data may be used todetermine a micro-moment value using a micro-moment algorithm. Themicro-moment value may then be used to predict consumer behavior and toengage the consumer based on that predicted behavior. In someembodiments, a merchant may receive the results of data analyses fromthe digital marketing platform 112 in order to aide in engagingcustomers to the merchant's online marketplace(s).

The digital marketing platform 112 may include one or more instructionmodules including a control module 114 that, generally, may includeinstructions to cause a processor 116 of a data processing server 118 tofunctionally communicate with a plurality of other computer-executablesteps or modules 114A and 114B. These modules 114, 114A-B may includeinstructions that, upon loading into the server memory 120 and executionby one or more computer processors 116, provide the digital marketingplatform with a dynamic data record of customer behavior. A first datarepository 122 may include digital marketing data profiles 122A thateach include various pieces of data to describe a profile of a consumeror customer and potential beneficiary of the digital marketing platform112. A second data repository 123 may include a plurality of dynamicdata records, for example, a first dynamic data record 123A, a seconddynamic data record 123B, etc., corresponding to each digital marketingdata profile 122A. The records 123A may each include various pieces ofdata to describe transactions or other online behaviors of a consumer orpotential customer benefiting from the digital marketing platform 112.In some embodiments, the records 123A and or B may include a time, anamount, a location, a route, a purchase category, photo data, a medium,content data, and other data as described herein.

The records 123A may be compiled from the online actions of a consumerfrom a variety of data streams. For example, the records 123A mayinclude syntactic information to allow the system 100 to determine adegree of probability that one or more online actions corresponding to aconsumer will occur again as well as how many times that action hasoccurred related to a consumer. The data streams associated with therecords 123A may include data from the DoubleClick® service provided byGoogle Inc., social media outlets, merchants, mapping services, andother services that monitor and collect online data with user consentthat may be matched to a user or consumer immediately or, with furtheranalysis, may be matched to a user.

The customer digital content browser may include any components that areused by a consumer to complete online transactions, participate insocial media, use mobile applications, browse the internet, or otherwiseparticipate in behavior contributing to data gathered by the digitalmarketing platform 112. For example, the customer digital contentbrowser 106 may include a terminal 140 that is used by one or morecomputing devices 138 to gather customer behavioral data. The terminal140 may include both a memory 142 and processor 144 to executeinstructions to send the customer information and other behavioral datato the digital marketing platform 112. In some embodiments, thecomputing devices may send data directly to the digital marketingplatform without including a terminal.

A customer engagement module 124 of the digital marketing platform 112may include various instructions that, upon execution by the processor116, facilitate execution of customer engagement. The module 124 mayinclude instructions that, upon loading into the server memory 120 andexecution by one or more computer processors 116, allow the digitalmarketing platform to draw upon data profiles 122A to execute amarketing, promotional communication, offer, or other consumerengagement using, for example, data from the data profile as describedherein and also coordinate with the control module 114 and a dynamictransaction records module 130 to permit interaction with a dynamic datarecord 123A.

The control module 114 may also include instructions to coordinateexecution of other instructions to link photos, reviews, social mediacapabilities, and behavioral data to a data record 123A. For example, alink module 114A may include instructions to cause a dynamic recordsmodule 130 stored in a memory 132 on a merchant computing device 128 todisplay a plurality of interfaces (e.g., 130A-E, 131A-D) within adisplay of the user computing device 128. In some embodiments, thedisplay may include a browser or other application stored in the memory132 and executed on a processor 134 of the computing device 128 todisplay an output of the dynamic data records module 130.

The dynamic data records module 130 may include several elementsincluding a dynamic consumer controls module 130A, a reviews module130B, a loyalty platform module 130C, an a transaction record module130D, and an alerts module 130E which may include several sub-modules toimplement particular functions with a single data record 123A or tocollectively display information related to a plurality of data records123A and 123B, etc. For example, the transaction record module 130D mayinclude several sub-modules including a payment module 131A, a socialmedia module 131B, a micro transactions module 131C, and a transactiongallery module 131D. Any of the interfaces or modules stored in thememory 132 may be used to configure the user computing device 128 tofacilitate both creating dynamic transaction report records 123A andcompleting the actions described herein that may be performed with therecords 123A. In other embodiments, one or more of the interfaces andmodules (i.e., 130, 130A, 130B, 130C, 130D, 130E, 131A, 131B, 131C,131D) may be stored in a memory of the payment processing system 112 ormultiple computing devices in a cloud-based model of execution andserved to the computing device 128 via the network 111 when requested.

While the dynamic transaction record 123A is described as includingvarious different types of data, those skilled in the art will recognizethat the record 123A may include other types of data that could berelated to consumer behavior such as physical distance from othertransactions, number of transactions in the area, similar productinformation, discounts or coupon information for items related to thetransaction, etc.

In some embodiments, the system 100 described above with reference toFIG. 1 may be implemented to determine substantially favorable momentsin which to effectively engage a customer. These “micro-moments” may betimes in which a consumer may be found to be demonstrating its intentclearly based on gathered data. In some embodiments, the system 100 mayuse a wide variety of consumer behavioral data to implement amicro-moment value algorithm (MMVA) to predict the optimal moment toengage a customer with a message or other communication relevant to thecustomer's micro-moment intent. These intentions could be, for example,intention to purchase a product online, book a flight or hotel, makedinner reservations, or any other online activity. In some embodiments,the MMVA may use a variety of machine learning algorithms in developingits results. For example, the MMVA may use Bayesian Network graphicalmodels, artificial neural networks, support vector machines, etc. Insome embodiments, the control module 114 may perform calculationsrelated to the MMVA.

In some embodiments, the MMVA can be expressed mathematically asEquation 1, below:

$\begin{matrix}{{MMV}_{1} = {\sum\limits_{c = 1}^{p}{\sum\limits_{e = 1}^{y}{\left( \frac{RCV}{Y_{i,t}} \right)*\left( \frac{\Pi \mspace{14mu} {radians}}{{ch}\; 5} \right)}}}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

The MMV notations in Eq. 1 may represent the following as listed inTable 1, below.

TABLE 1 Variable Definition p Customer Index y Click Index c ConversionIndex e Customer Experience Type ch5 Channels or devices RCV Real-timeCustomer Journey Value Y Contextual Signals Value t Synaptic WeightRadians JourneyIn some embodiments, the Customer Index may be the number of profiles orpersonas or a unique customer corresponding to a micro-moment, the ClickIndex may be a statistical measure of changes of individual Data Pointsin the click rate or a primary key for a cookie or other piece ofsoftware that records a micro-moment, and the Conversion Index may be astatistical measure of changes of individual Data Points in theconversion rate or a particular number used to identify a micro-momentas a conversion. In some embodiments, the Customer Experience Type maybe a measure of whether the customer experience is complete, where e=1,or an incomplete experience, where e=0. A conversion for a signal mayindicate a complete customer experience or e=1 whereas anything otherthan a conversion may indicate a value of 0 for the customer experiencetype. The Channel or devices may be different vectors for customerinterfacing; for example, mobile phones, smart phones, desktop or laptopcomputers, internet-capable television, etc. In some embodiments, theReal-time Customer Journey Value (RCV) may represent click throughDomains; for example, the number of signals gathered from consumerbehavior based on clicking behavior while browsing or using a mobileapp. In other embodiments, the RCV includes a random number that thesystem 100 uses to index the micro-moment. Contextual Signals Value maybe synaptic signals through a graph of neurons; for example, as attainedthrough an artificial neural network (ANN). In other embodiments, theContextual Signals Value (Y) may be a value representing the context ofthe value associated with the micro-moment (i.e., a first value if themicro-moment was a click to complete a purchase, a second value if themicro-moment was a click to read an article or to send an email, deletean email, etc.). In some embodiments, the Synaptic Weight may be anumber applied for weighting purposes in the ANN. The Journey, in someembodiments, may be expressed in radians and may be the value of thesynaptic signals.

The micro-moment value (MMV) equation may be broken down into threecomponents: the conversion rate, the signal of the click rate throughdomains, and the radian rate per channel. In some embodiments, theexpression:

$\sum\limits_{e = 1}^{y}\left( \frac{RCV}{Y_{i,t}} \right)$

may represent the signal of the click rate through domains. In someembodiments, the expression:

$\sum\limits_{c = 1}^{p}$

may represent the conversion rate, particularly when applied to thesignal of the click rate through domains. In some embodiments, theexpression:

$\left( \frac{\Pi \mspace{14mu} {radians}}{{ch}\; 5} \right)$

may represent the click rate across channels or click rate per channel.

In some embodiments, the artificial neural network model may berepresented by the diagram 200 illustrated in FIG. 2. In suchembodiments, the inputs 202 (e.g., x₁-x_(n)) may be any data pointsgathered from consumer behavior, as described in further detail withreference to FIG. 3. Weights 204 (e.g., w_(1j)-w_(nj)) may be applied tothe inputs depending on the particular design of the artificial neuralnetwork. The result may be combined into a transfer function 206, theresult of which may be a net input 208 (e.g., net) fed into anactivation function 210. The activation junction 210 may output athreshold 214 (e.g., Θ_(j)) and an activation 212 (e.g., o_(j)). Theoutput of the artificial neural network may, in some embodiments, berepresented by a perceptron equation, as shown in Equation 2, below:

Output=(1,_(Σ) _(t=0) _(n) _(w) ₁ _(*x) ₁ _(>c) _(b,−1) otherwise  Eq. 2

In Eq. 2, w may be determined from a Hebb Synapse simulated learningequation, an embodiment of which is shown in Equations 3 and 4, below:

w _(j) ^(k+1) =w _(j) ^(k) +Δw _(j)  Eq. 3

Δw _(j)=α·(t−o)·i  Eq. 4

-   -   where: t=Target    -   o=Output of the neural network    -   α=Learning rate constants, between 0 and 1    -   i=Input of the weight

As used herein, a “radian” is a data container used in the abovealgorithms to mathematically represent segments or “micro-moments” auser's journey. Past systems and methods of analyzing an individual'sonline presence to predict future online actions have used theindividual's entire online history to make universal predictions aboutany particular moment of a user's internet presence. These past systemhave analyzed a customer's entire journey as the most effective way tomarket to a user based on a universal data set. In these past systemsand methods, the accumulation of large amounts of data often leads tocumbersome calculations and inaccurate results as infinite data may leadto infinite results. The radian may provide definition to the limitlessdata related to web and other network interactions by providing aframework to calculate micro-moments as described herein. Rather thanusing all data that has been tracked for a customer to make a predictionabout a future action, the radian contains a small amount of trackeddata related to the time and duration of the action, a URL or othernetwork location for the action, and a type of action. Framing eachmicro-moment or signal as a radian may allow predicting a follow-onaction based on a set of signals corresponding to actions that precedethe predicted action. Predicted events having the highest value for theMMVA, i.e., the highest duration for a particular action, will providethe best opportunity to reach the user for effective marketing.

In mathematics, a radian is an angle whose corresponding arc in a circleis equal to the radius of the circle. A circle has just more than sixradians. A customer's complete “journey” may begin with an initial clickor other tracked action related to a product or service at a domain orother “channel” and end with a purchase or other final action related tothe domain, channel, good or service, etc. In some embodiments, acomplete revolution of a circle is 2π radians and is analogous to acomplete customer journey. In these embodiments, a customer's journeymay be represented as a complete revolution of a circle and discreteevents or signals related to the journey may be represented as radians.Values for each signal or “radian” may be assigned as degrees of acircle relative to the complete customer journey as shown in Table 1A,below. Where the final action In a series of actions or signals analyzedby Equation 1 and related to the domain, channel, good or service is adesired action (e.g., a conversion, purchase, etc.), then the value forthe radian will reflect a complete journey or 360°. In furtherembodiments, where the final action related to the domain, channel, goodor service is not a conversion or purchase, then the value for theradian will reflect something less than a complete journey or less than360°. With brief reference to FIG. 4, each radian may include data for asingle signal, action, or micro-moment. By stitching together severalmicro-moments based on known actual customer journey 412 for the userand an audience trend 414, the MMVA result may be a prediction 416 ofthe user's journey. Table 1A indicates values for the radian as a numberof degrees for a customer journey, where a complete customer journeyending in conversion having ten total “signals” corresponding to a usermay include ten “radians” or, as shown in the table below, a completejourney including sixteen signals may have sixteen different radianvalues.

TABLE 1A Radians Degrees 0, 2π 0°, 360°  π/6  30°  π/4  45°  π/3  60° π/2  90° 2π/3 120° 3π/4 135° 5π/6 150° π 180° 7π/6 210° 5π/4 225° 4π/3240° 3π/2 270° 5π/3 300° 7π/4 315° 11π/6  330°

FIG. 3 illustrates one embodiment of an example customer journey usecase 300 that may result in signals that make up each radian torepresent the data used as inputs for the MMVA. In this embodiment, thecustomer journey 302 is represented by an arrow moving and may representpassage of time. Points along the arrow may be single points in timewhere a customer performs an action 301, represented by hash marks alongthe journey arrow, that results in a signal. This particular embodimentincludes 14 actions and, therefore, 14 signals, but any number ofsignals may be gathered and used. Each signal may be interpreted by thedigital marketing platform 112 as data forming the customer profile. Inthis example, two channels (browser 304 and mobile app 306) are used bythe customer, though it should be understood that fewer or more than twochannels. The journey 302 includes multiple browser actions 308conducted by the customer using an internet browser channel, such as ona desktop or laptop computer. In this embodiment, each of the browsersignals are performed in the same domain. Some types of actionsresulting in signals may include visiting social media sites, such asFacebook®).com, clicking on advertisements while on the social mediasite, viewing a product, adding a product to a shopping cart, andexiting the website. The browser signals 308 may also include a sequenceof signals 312. In this embodiment, the sequence of signals 312 is fourconsecutive signals grouped together after a customer has clicked on anadvertisement. These signals may be 1) redirecting to the advertiser'ssite, 2) firing a cookie, 3) an impression, and 4) clicking on apromotion.

The journey 302 also includes mobile signals 314 taken by a customer ona mobile app 306 accessed on a mobile device, such as a smartphone ortablet. As used herein, the term “signals” may include datacorresponding to a user and online activity such as a click (i.e.,clicking on a URL), an impression (e.g., a user typing in a specific URLand causing a browser to initiate a redirect action to reach a domain),a pixel (e.g., selecting images including at least one pixel having atag and/or other data for the system 100 to track for a user), a cookie(e.g., a small piece of data sent from a website and stored on theuser's computer by the user's web browser while the user is browsing toremember state information such as items added in the shopping cart inan online store or to record the user's browsing activity), etc. Somedata included in a signal 314 may include a beginning time and an endingtime at a website (e.g., a time between mouse events or from landing ata URL to the browser initiating a redirect action) a duration (e.g., anindication of user activity, how much time and attention the user paysto web content corresponding to a signal 312 at a particular domainand/or using a particular channel, mouse events, browser actions, etc.),a domain name, a tag number, a pixel name, a cookie name and value, andother data corresponding to the user's browsing activity. The cookie mayallow the system 100 to collect syntactic information that reflects adegree of probability for a future action by the user and also indicateshow often the user will initiate a type of network action. Thus, themicro-moment value may predict a significant duration for a particularnetwork action or Internet domain activity and, thus, the most optimaltime to present advertising targeted to the user to influence the user'sjourney.

In some embodiments, each of the mobile signals 314 is performed in thesame domain as one another. Some types of actions resulting in mobilesignals 314 may include searching for similar products on a searchengine, such as Google, being redirected to the product website from thesearch engine, landing on the product description page, adding theproduct to the shopping cart, and checking out to purchase the product.The mobile signals 314 may include a sequence of mobile signals 316that, in this case, are three consecutive signals grouped together. Inthe example illustrated in FIG. 3, the result of the browser signals 308and the mobile signals 314 is a total of fourteen signals on the journey302. These fourteen signals may be used as the RCV in the MMV Eq. 1, asshown in the example below.

The following is an example of a calculation made using the micro-momentvalue algorithm using example inputs that, in practice, would be theresult of data gathering through system 100 of FIG. 1 for use in thedigital marketing platform 112. It should be understood that the inputvalues used herein are for the sake of example only, and do not in anyway limit the values available for use in the MMVA that can be gatheredand used by the digital marketing platform 112. In this exampleembodiment, the data values for the MMVA are as follows in Table 2:

TABLE 2 Variable Example Value p 3000 y 10 c 1 e 1 or i ch5 1 RCV 14 Y10 t Synaptic Weight Radians 360° or 2π

Using p=3000 and y=10, the conversion rate and the signal of the clickrate through domains becomes:

${\sum\limits_{c = 1}^{p}{\sum\limits_{e = 1}^{y}\left( \frac{RCV}{Y_{i,t}} \right)}} = {\sum\limits_{c = 1}^{3000}{\sum\limits_{e = 1}^{10}\left( \frac{14}{10_{i,t}} \right)}}$

which can be expressed as:

${\sum\limits_{c = 1}^{p}{\sum\limits_{e = 1}^{y}\left( \frac{RCV}{Y_{i,t}} \right)}} = {\sum\limits_{c = 1}^{3000}\left( {\frac{14}{10_{i,1}} + \frac{14}{10_{i,2}} + \frac{14}{10_{i,3}} + \ldots + \frac{14}{10_{i,9}} + \frac{14}{10_{i,10}}} \right)}$

Further, adding the click rate per channel, the MMVA may then beexpressed as:

$\begin{matrix}{{MMV}_{1} = {\sum\limits_{c = 1}^{3000}{\sum\limits_{e = 1}^{10}{\left( \frac{14}{10_{i,t}} \right)*\left( \frac{\Pi \; 2\pi}{1} \right)}}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

FIG. 4 illustrates an example graph 400 of the micro-moment predictions402 over time, indicated on the horizontal axis as days of the week 401.The results of the MMVA shown in FIG. 4 are plotted as the micro-momentpredictions 402 expressed in the example culminating in Equation 5,above. The graph 400 in FIG. 4 additionally shows the actual customerjourney 404 and audience trends 406. One goal of the micro-momentspredictions 402 calculated using the MMVA is to replicate the actualcustomer journey 404. This allows the digital marketing platform 112 tomost accurately predict the micro-moment for engagement with thecustomer. In some embodiments, these “key” micro-moments may be at thepeaks of the MMVA plot. For example, in FIG. 4, the micro-momentpredictions 402 may have a first predicted key moment 408 at a peak inthe micro-moment predictions plot. As shown in the graph 400, a plot ofthe micro-moment predictions may have a second predicted key moment 410at another peak in the micro-moment predictions plot. In the illustratedembodiment of FIG. 4, these peaks may correspond to an amount of timecorresponding to a signal (e.g., signals 312 with reference to FIG. 3)in the various plots of the graph 400 (e.g., the actual customer journey412 plot, the audience trends 414 plot, the micro-moments predictionsplot 416, etc.).

With further reference to FIG. 4, the method 500 generally and Eq. 1 inparticular as described herein may result in values for micro moments topredict user trends for engaging a user at a favorable time. The graph400 includes a y-axis having micro-moment predictions 402 and an x-axishaving a time period 403 corresponding to the micro-moment prediction402. In some embodiments, the inputs to Eq. 1 may result in micro-momentpredictions 402 of a measure of time (e.g., milliseconds, etc.) of userbehavior corresponding to a signal (e.g., signals 312 of FIG. 3) for theparticular time period 403. For example, the first predicted key moment408 may indicate a duration of about 500,000 milliseconds correspondingto Monday morning at a particular URL using a particular channel (i.e.,a type of device and/or method for reaching the URL).

FIG. 5 illustrates an embodiment of a method 500 for predicting amicro-moment using the MMVA through the digital marketing platform 112.Each step of the method may be performed on a server or other computingdevice including instructions that, when executed by a processor,perform the action or block described herein. At block 502, the methodmay include receiving the real-time consumer journey data via thedigital marketing platform 112. At block 504, the method may includereceiving contextual signals value data, and at block 506 may includedetermining a click index value. At block 508, the method may includedetermining a customer experience type value. At block 510, the methodmay include calculating a signal of click rate through domains using thereal-time consumer journey data, the contextual signals value data, theclick index value, and the customer experience type value. At block 512,the method may include receiving radian data through the digitalmarketing platform 112. At block 514, the method may include receivingchannel data. At block 516, the method may include calculating a radianrate per channel using the radian data and the channel data. At block518, the method may include receiving customer index data via thedigital marketing platform 112. At block 520, the method may includereceiving conversion index data. At block 522, the method may includecalculating a conversion rate using the customer index, the conversionindex, and the signal of the click rate through domains. At block 524,the method may include calculating a micro-moment value using theconversion rate and the radian rate per channel. In some embodiments, atblock 526, the method may include engaging a customer based on thecalculated micro-moment value. In some embodiments, engaging thecustomer may include, but is not limited to, sending a promotionaloffer, sending an electronic coupon, or sending a targetedadvertisement, etc., at the predicted time and network locationindicated by the MMVA.

In some embodiments, the system 100 may include a module to determine avalue for each customer. For example, an algorithm may determine whethera customer is going to be profitable or not and for how long. Too, analgorithm may predict the monetary value associated with a customerrelationship. Equation 6 illustrates one example of a customer lifetimevalue (“CLV”) algorithm:

$\begin{matrix}{{CLV} = {{\sum\limits_{m = 1}^{t}\left( \frac{{Future}\mspace{14mu} {Contribution}\mspace{14mu} {Margins}}{{Historical}\mspace{14mu} {Lifetime}\mspace{14mu} {Values}*p} \right)} - \left( \frac{{Cost}\mspace{14mu} {of}\mspace{14mu} {Acquisition}}{{CL}^{(m)}} \right)}} & {{Eq}.\mspace{14mu} 6}\end{matrix}$

In Equation 6, m=the micro-moment index; c=Customer Index; p=totalnumber of purchases in a period of time; t=number of time period the CLVis being calculated; and CL=the Customer Loyalty Index. The CLValgorithm of Equation 6 may allow observation of variousindividual-level buying patterns from the past and find the variouscustomer stories in the data set. It may also allow understanding ofwhich patterns correspond with valuable customers and which patternscorrespond with customers who are opting out. As new customers join asystem implementing the CLV algorithm of Equation 6, the system (e.g.,system 100) may match the new customer to patterns that are recognizedby the CLV algorithm.

In some embodiments, the system 100 may also include a module todetermine which impressions will best meet the advertising performancemetrics. For example, an algorithm may optimize timing for real-timebidding (“RTB”) on customer impressions within a website for a merchant.For example, advertising campaign budget constraints may be given asimpression delivery goals q_(j). An impression group i may be defined asa (placement, user) tuple, at which level both click-through-rate(“CTR”) prediction P_(ij) and inventory control represented by Equation7 may be performed:

$\begin{matrix}{{\sum\limits_{j}^{1}x_{ij}} \leq h_{i}} & {{Eq}.\mspace{14mu} 7}\end{matrix}$

Given the above, the cost term w_(i) will be zero since impressions arefrom the inventory. Thus, the revenue lift and the CTR lift may berepresented by Equations 8 and 9, below:

$\begin{matrix}{{{Revenue}\mspace{14mu} {lift}} = {\frac{y^{\prime}}{y} = \frac{{\Sigma \; t},i,{{{jx}_{ij}^{\prime}(t)}p_{ij}q_{j}}}{{\Sigma \; t},i,{{{jc}_{ij}^{\prime}(t)}q_{j}}}}} & {{Eq}.\mspace{14mu} 8} \\{{{CTR}\mspace{14mu} {lift}} = {\frac{{CTR}^{\prime}}{CTR}\frac{{\Sigma \; t},i,{{{jx}_{ij}^{\prime}(t)}{p_{ij}/\Sigma}\; t},i,{{jx}_{ij}^{\prime}(t)}}{{\Sigma \; t},i,{{{jc}_{ij}^{\prime}(t)}{q_{j}/\Sigma}\; t},i,{{jx}_{ij}^{\prime}(t)}}}} & {{Eq}.\mspace{14mu} 9}\end{matrix}$

The timing for RTB may also be optimized by the following pseudo code:

Input: q_(j),g_(j),α_(j),∀j Output: x_(ij),β_(i),∀i,j 1 begin 2  | G ←∅; 3  | foreach impression i from a stream do 4  |  | p_(ij) =p(click|i,j),∀j; 5  |  | v_(ij) ← p_(ij)q_(j),∀j; 6  |  | j* ←argmax_(j∉G) (v_(ij) − α_(j)); 7  |  | if (v_(ij*) − α_(j*)) > 0 then 8 |  |  | x_(ij*) ← 1; 9  |  |  | x_(ij) ← 0,∀j ≠ j*; 10  |  |  | β_(i) ←v_(ij*) − α_(j*); 11  |  |  | if Σ_(i′)x_(i′j*) = g_(j*) then 12 |  |  |  | G ← G ∪j*; 13  |  |  | end 14  |  | end 15  |  | α_(j) ←UpdateAlpha(α_(j)),∀j; 16  | end 17 end

In some embodiments, the system 100 may also include a module toevaluate each customer impression based on its predicted probability toachieve a goal of the advertising campaign. For example the module mayevaluate Equations 10 and 11, below.

$\begin{matrix}{\mspace{85mu} {{mfb} = \left. \frac{\epsilon + \gamma - {P\left( B_{i*} \right)}}{\gamma} \middle| \frac{\gamma - {P\left( B_{i*} \right)}}{\gamma} \right.}} & {{Eq}.\mspace{11mu} 10} \\{\mspace{79mu} {{V_{cpm} = {C_{explore} + C_{panic} + {\left( n_{exploit}^{*} \right)T\text{?}}}}{\text{?}\text{indicates text missing or illegible when filed}}}} & {{Eq}.\mspace{11mu} 11}\end{matrix}$

Where C_(panic)=a number of impressions won during a panic;C_(exploit)=a number of impressions won during exploitation; andC_(explore)=a number of impressions won during exploration. Costs perthousand (“CPM”) may also be analyzed by the following pseudocode:

50: If g_(remain) ≤ 0 or j ≥ n then Terminate. 51: B_(final) ← B_(i*).52:$\left. A\;\leftarrow{\frac{g_{remain}}{P_{i^{*}}\left( {n - j} \right)}.} \right.$53: if P_(i*) (n − j) > g_(remain) and T_(i*)g_(remain) > budget then54:  Sort p ∈ S_(i*): define q_(k) to be the kth smallest p in S_(i*).55:  $\left. k_{s}\leftarrow\left\lceil {\frac{g_{remain}}{n - j}m} \right\rceil \right.$56:  ${{for}\mspace{14mu} k} = \left. {1\mspace{14mu} {to}\mspace{14mu} {S_{i^{*}}}\mspace{14mu} {do}\mspace{14mu} g_{k}}\leftarrow{\frac{1}{k}{\sum\limits_{i = 1}^{k}{q_{i}.}}} \right.$57:   $\left. t^{*}\leftarrow{\frac{budget}{g_{remain}}.} \right.$ 58: k_(p) ← min_(k:g) _(k) _(≥t*) k. 59:  k* ← max(k_(s), k_(p)). 60: B_(final) ← q_(k*). 61:  $\left. A\;\leftarrow{\frac{k^{*}}{m}.} \right.$ 62:  $\left. A\leftarrow{\frac{g_{remain}}{A\left( {n - j} \right)}.} \right.$63: end if 64: while More rounds and g_(remain) > 0 do 65:  BidB_(final) with probability A, 0 otherwise. 66:  If Bid won theng_(remain) ← g_(remain) − 1. 67: end whilewhere optimized daily or other periodic budget updates may be calculatedand posted, as needed, to meet each campaign goal.

FIG. 6 is a high-level block diagram of an example computing environment600, for example, for linking the digital marketing platform to frontend components that may run the customer digital content browser and/orthe merchant digital content system. The computing device 601 mayinclude a server (e.g., the data processing server 118), a mobilecomputing device (e.g., computing device 128, a cellular phone, a tabletcomputer, a Wi-Fi-enabled device or other personal computing devicecapable of wireless or wired communication), a thin client, or otherknown type of computing device. As will be recognized by one skilled inthe art, in light of the disclosure and teachings herein, other types ofcomputing devices can be used that have different architectures.Processor systems similar or identical to the example systems andmethods for linking dynamic information, such as customer data, to adata record may be used to implement and execute the example systems ofFIG. 1. Although the example system 600 is described below as includinga plurality of peripherals, interfaces, chips, memories, etc., one ormore of those elements may be omitted from other example processorsystems used to implement and execute the example system for linkingdynamic information to a digital marketing platform. Also, othercomponents may be added.

As shown in FIG. 6, the computing device 601 includes a processor 602that is coupled to an interconnection bus. The processor 602 includes aregister set or register space 604, which is depicted in FIG. 6 as beingentirely on-chip, but which could alternatively be located entirely orpartially off-chip and directly coupled to the processor 602 viadedicated electrical connections and/or via the interconnection bus. Theprocessor 602 may be any suitable processor, processing unit ormicroprocessor. Although not shown in FIG. 6, the computing device 601may be a multi-processor device and, thus, may include one or moreadditional processors that are identical or similar to the processor 602and that are communicatively coupled to the interconnection bus.

The processor 602 of FIG. 6 is coupled to a chipset 606, which includesa memory controller 608 and a peripheral input/output (I/O) controller610. As is well known, a chipset typically provides I/O and memorymanagement functions as well as a plurality of general purpose and/orspecial purpose registers, timers, etc. that are accessible or used byone or more processors coupled to the chipset 606. The memory controller608 performs functions that enable the processor 602 (or processors ifthere are multiple processors) to access a system memory 612 and a massstorage memory 614, that may include either or both of an in-memorycache (e.g., a cache within the memory 612) or an on-disk cache (e.g., acache within the mass storage memory 614).

The system memory 612 may include any desired type of volatile and/ornon-volatile memory such as, for example, static random access memory(SRAM), dynamic random access memory (DRAM), flash memory, read-onlymemory (ROM), etc. The mass storage memory 614 may include any desiredtype of mass storage device. For example, if the computing device 601 isused to implement a module 616 (e.g., the various modules link dynamicinformation, such as photographs, to a payment device transaction recordand to create the dynamic transaction record and other modules as hereindescribed). The mass storage memory 614 may include a hard disk drive,an optical drive, a tape storage device, a solid-state memory (e.g., aflash memory, a RAM memory, etc.), a magnetic memory (e.g., a harddrive), or any other memory suitable for mass storage. As used herein,the terms module, block, function, operation, procedure, routine, step,and method refer to tangible computer program logic or tangible computerexecutable instructions that provide the specified functionality to thecomputing device 601 and the system 100. Thus, a module, block,function, operation, procedure, routine, step, and method can beimplemented in hardware, firmware, and/or software. In one embodiment,program modules and routines are stored in mass storage memory 614,loaded into system memory 612, and executed by a processor 602 or can beprovided from computer program products that are stored in tangiblecomputer-readable storage mediums (e.g. RAM, hard disk, optical/magneticmedia, etc.).

The peripheral I/O controller 610 performs functions that enable theprocessor 602 to communicate with a peripheral input/output (I/O) device624, a network interface 626, a local network transceiver 628, (via thenetwork interface 626) via a peripheral I/O bus. The I/O device 624 maybe any desired type of I/O device such as, for example, a keyboard, adisplay (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT)display, etc.), a navigation device (e.g., a mouse, a trackball, acapacitive touch pad, a joystick, etc.), etc. The I/O device 624 may beused with the module 616, etc., to receive data from the transceiver628, send the data to the backend components of the system 100, andperform any operations related to the methods as described herein. Thelocal network transceiver 628 may include support for a Wi-Fi network,Bluetooth, Infrared, cellular, or other wireless data transmissionprotocols. In other embodiments, one element may simultaneously supporteach of the various wireless protocols employed by the computing device601. For example, a software-defined radio may be able to supportmultiple protocols via downloadable instructions. In operation, thecomputing device 601 may be able to periodically poll for visiblewireless network transmitters (both cellular and local network) on aperiodic basis. Such polling may be possible even while normal wirelesstraffic is being supported on the computing device 601. The networkinterface 626 may be, for example, an Ethernet device, an asynchronoustransfer mode (ATM) device, an 802.11 wireless interface device, a DSLmodem, a cable modem, a cellular modem, etc., that enables the system100 to communicate with another computer system having at least theelements described in relation to the system 100.

While the memory controller 608 and the I/O controller 610 are depictedin FIG. 6 as separate functional blocks within the chipset 606, thefunctions performed by these blocks may be integrated within a singleintegrated circuit or may be implemented using two or more separateintegrated circuits. The computing environment 600 may also implementthe module 616 on a remote computing device 630. The remote computingdevice 630 may communicate with the computing device 601 over anEthernet link 632. In some embodiments, the module 616 may be retrievedby the computing device 601 from a cloud computing server 634 via theInternet 636. When using the cloud computing server 634, the retrievedmodule 616 may be programmatically linked with the computing device 601.The module 616 may be a collection of various software platformsincluding artificial intelligence software and document creationsoftware or may also be a Java® applet executing within a Java® VirtualMachine (JVM) environment resident in the computing device 601 or theremote computing device 630. The modeling module 620 and the executionmodule 622 may also be “plug-ins” adapted to execute in a web-browserlocated on the computing devices 601 and 630. In some embodiments, themodule 616 may communicate with back end components 638 such as thebackend components 110 of FIG. 1 via the Internet 636.

The system 600 may include but is not limited to any combination of aLAN, a MAN, a WAN, a mobile, a wired or wireless network, a privatenetwork, or a virtual private network. Moreover, while only one remotecomputing device 630 is illustrated in FIG. 6 to simplify and clarifythe description, it is understood that any number of client computersare supported and can be in communication within the system 100.

FIG. 7 is a high-level block diagram of the various components of thesystem 100.

Additionally, certain embodiments are described herein as includinglogic or a number of components, modules, or mechanisms. Modules mayconstitute either software modules (e.g., code or instructions embodiedon a machine-readable medium or In a transmission signal, wherein thecode is executed by a processor) or hardware modules. A hardware moduleis tangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more hardware modules of a computer system (e.g., aprocessor or a group of processors) may be configured by software (e.g.,an application or application portion) as a hardware module thatoperates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where thehardware modules comprise a general-purpose processor configured usingsoftware, the general-purpose processor may be configured as respectivedifferent hardware modules at different times. Software may accordinglyconfigure a processor, for example, to constitute a particular hardwaremodule at one instance of time and to constitute a different hardwaremodule at a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve physical manipulation of physicalquantities. Typically, but not necessarily, such quantities may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “some embodiments” or “an embodiment” or“teaching” means that a particular element, feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. The appearances of the phrase “in someembodiments” or “teachings” in various places in the specification arenot necessarily all referring to the same embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not Indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

Further, the figures depict preferred embodiments for purposes ofillustration only. One skilled In the art will readily recognize fromthe following discussion that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein

Upon reading this disclosure, those of skill In the art will appreciatestill additional alternative structural and functional designs for thesystems and methods described herein through the disclosed principlesherein. Thus, while particular embodiments and applications have beenillustrated and described, it is to be understood that the disclosedembodiments are not limited to the precise construction and componentsdisclosed herein. Various modifications, changes and variations, whichwill be apparent to those skilled in the art, may be made in thearrangement, operation and details of the systems and methods disclosedherein without departing from the spirit and scope defined in anyappended claims.

1. A computer-implemented method for determining a micro-moment value,the method comprising: receiving a plurality of signals from a pluralityof online actions, the plurality of signals corresponding to an onlineprofile for a consumer (p), and each of the plurality of signalsincluding one or more of a past duration, a past network location, apast time period, and a past action type; determining a predictedmicro-moment value based on the plurality of signals, the predictedmicro-moment value predicting one or more of a future duration, a futurenetwork location, and a future time period for a further online actionfor the consumer, the micro-moment value consisting of:${MMV}_{1} = {\sum\limits_{c = 1}^{p}{\sum\limits_{e = 1}^{y}{\left( \frac{RCV}{Y_{i,t}} \right)*\left( \frac{\Pi \; {radians}}{channel} \right)}}}$wherein: $\sum\limits_{c = 1}^{p}$ includes a conversion index (c) forthe plurality of signals that include a conversion (c=1) across theplurality of online profiles (p), and consists of:$\sum\limits_{{{Conversion}\mspace{14mu} {Index}\mspace{14mu} {(c)}} = 1}^{{Total}\mspace{14mu} {Number}\mspace{14mu} {of}\mspace{14mu} {Profiles}\mspace{11mu} {(p)}}$wherein: $\sum\limits_{e = 1}^{y}\left( \frac{RCV}{Y_{i,t}} \right)$includes a click rate through domains, wherein the click rate throughdomains consists oft$\sum\limits_{{{Customer}\mspace{14mu} {Experience}\mspace{20mu} {Value}\mspace{14mu} {(e)}} = {1\mspace{14mu} {or}\mspace{14mu} 0}}^{{Click}\mspace{14mu} {Index}\mspace{11mu} {(y)}}\frac{{Real}\text{-}{time}\mspace{14mu} {Customer}\mspace{14mu} {Journey}\mspace{14mu} {Value}\mspace{14mu} ({RCV})}{{Contextual}\mspace{14mu} {Signals}\mspace{14mu} {Value}\mspace{14mu} (Y)}$wherein the click index (y) includes a primary key for each of theplurality of signals, the customer experience value includes a value of1 for each of the plurality of signals that includes the conversion or avalue of 0 for each of the plurality of signals that does not includethe conversion, the RCV includes an index value for each of theplurality of signals, and Y includes a value based on the past actiontype; wherein: $\left( \frac{\Pi \; {radians}}{channel} \right)$Includes a click rate per channel and the click rate per channelconsists of:$\left( \frac{\Pi \; {radians}\mspace{14mu} {value}}{channel} \right)$the radian value consists of a value based on a total number for theplurality of signals and the channel equals a value corresponding to atype of device accessing the domain; and engaging the customer when themicro-moment value reaches a duration and a time period corresponding toa predicted key moment in a plot of the predicted micro-moment values,wherein the micro moment value indicates an optimal time and networklocation to engage the customer.
 2. The computer-implemented method ofclaim 1, wherein the plurality of profiles (p) includes a total numberof profiles for the first plurality of signals and the second pluralityof signals.
 3. The computer implemented method of claim 2, wherein theconversion index (c) includes a statistical measure of changes in theplurality of signals.
 4. The computer implemented method of claim 3,wherein the real-time customer journey value (RCV) includes a totalcount of the first plurality of signals and the second plurality ofsignals.
 5. The computer-Implemented method of claim 4, wherein theclick index (y) includes the plurality of signals for the period of timefor each of the browser accessing the first domain and the mobile app incommunication with the second domain, wherein: the click index for theperiod of time for the browser accessing the first domain includes anaverage count of the first plurality of signals during the period oftime, and the click index for the period of time for the mobile app incommunication with the second domain includes an average count of thesecond plurality of signals during the period of time;
 6. Thecomputer-implemented method of claim 5, wherein the contextual signalsvalue (Y) predicts a further signal accessing the domain based on theplurality of signals, wherein the contextual signals value includes anoutput of an artificial neural network (ANN), and inputs to the ANNinclude the first plurality of signals, the second plurality of signals,weights applied to each of the first plurality of signals and the secondplurality of signals, a transfer function having the first plurality ofsignals, the second plurality of signals, and the weights as input tothe transfer function, an activation function including an output of thetransfer function.
 7. The computer-implemented method of claim 6,wherein an ANN output includes a perceptron equation.
 8. Thecomputer-implemented method of claim 7, wherein one or more synapses ofthe ANN includes a Hebb synapse.
 9. A system for determining amicro-moment value comprising: at least one processor, and at least onememory storing computer executable instructions that, when executed bythe at least one processor, cause the system at least to perform thesteps of: receiving a plurality of signals from a plurality of onlineactions, the plurality of signals corresponding to an online profile fora consumer (p), and each of the plurality of signals including one ormore of a past duration, a past network location, a past time period,and a past action type; determining a predicted micro-moment value basedon the plurality of signals, the micro-moment value predicting one ormore of a future duration, a future network location, and a future timeperiod for a further online action for the consumer, the micro-momentvalue consisting of:${MMV}_{1} = {\sum\limits_{c = 1}^{p}{\sum\limits_{e = 1}^{y}{\left( \frac{RCV}{Y_{i,t}} \right)*\left( \frac{\Pi \; {radians}}{channel} \right)}}}$wherein: $\sum\limits_{c = 1}^{p}$ includes a conversion index (c) forthe plurality of signals that include a conversion (c=1) across theplurality of online profiles (p), and consists of:$\sum\limits_{{{Conversion}\mspace{14mu} {Index}\mspace{14mu} {(c)}} = 1}^{{Total}\mspace{14mu} {Number}\mspace{14mu} {of}\mspace{14mu} {Profiles}\mspace{11mu} {(p)}}$wherein: $\sum\limits_{e = 1}^{y}\left( \frac{RCV}{Y_{i,t}} \right)$includes a click rate through domains click rate through domainsconsists of:$\sum\limits_{{{Customer}\mspace{14mu} {Experience}\mspace{20mu} {Value}\mspace{14mu} {(e)}} = {1\mspace{14mu} {or}\mspace{14mu} 0}}^{{Click}\mspace{14mu} {Index}\mspace{11mu} {(y)}}\frac{{Real}\text{-}{time}\mspace{14mu} {Customer}\mspace{14mu} {Journey}\mspace{14mu} {Value}\mspace{14mu} ({RCV})}{{Contextual}\mspace{14mu} {Signals}\mspace{14mu} {Value}\mspace{14mu} (Y)}$wherein the click index (y) includes a primary key for each of theplurality of signals, the customer experience value includes a value of1 for each of the plurality of signals that includes the conversion or avalue of 0 for each of the plurality of signals that does not includethe conversion, the RCV includes an index value for each of theplurality of signals, and Y includes a value based on the past actiontype; wherein: $\left( \frac{\Pi \; {radians}}{channel} \right)$includes a click rate per channel and the click rate per channelconsists of:$\left( \frac{\Pi \; {radians}\mspace{14mu} {value}}{channel} \right)$the radian value consists of a value based on a total number for theplurality of signals and the channel equals a value corresponding to atype of device accessing the domain; and engaging the customer when themicro-moment value reaches a duration and a time period corresponding toa predicted key moment in a plot of the predicted micro-moment values,wherein the micro moment value indicates an optimal time and networklocation to engage the customer.
 10. The system of claim 9, wherein theplurality of profiles (p) includes a total number of profiles for thefirst plurality of signals and the second plurality of signals.
 11. Thesystem of claim 10, wherein the conversion index (c) includes astatistical measure of changes in the plurality of signals.
 12. Thesystem of claim 11, wherein the real-time customer journey value (RCV)includes a total count of the first plurality of signals and the secondplurality of signals.
 13. The system of claim 12, wherein the clickindex (y) includes the plurality of signals for the period of time foreach of the browser accessing the first domain and the mobile app incommunication with the second domain, wherein: the click index for theperiod of time for the browser accessing the first domain includes anaverage count of the first plurality of signals during the period oftime, and the click index for the period of time for the mobile app Incommunication with the second domain includes an average count of thesecond plurality of signals during the period of time;
 14. The system ofclaim 13, wherein the contextual signals value (Y) predicts a furthersignal accessing the domain based on the plurality of signals, whereinthe contextual signals value includes an output of an artificial neuralnetwork (ANN), and inputs to the ANN include the first plurality ofsignals, the second plurality of signals, weights applied to each of thefirst plurality of signals and the second plurality of signals, atransfer function having as input to the transfer function the firstplurality of signals, the second plurality of signals, and the weights,and an activation function including an output of the transfer function.15. The system of claim 14, wherein an ANN output includes a perceptronequation.
 16. The system of claim 15, wherein one or more synapses ofthe ANN includes a Hebb synapse.